{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1 = np.array([[1, 2], [3, 4]], dtype='float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.zeros((6, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr3 = np.ones((6, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr4 = np.eye(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr5 = np.arange(0, 10, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr6 = np.linspace(0, 10, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr7 = np.random.randint(1, 100, 10)\n",
    "arr7[np.argmax(arr7)]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([1,2,3,2,3,4,3,4,5,6])\n",
    "y = np.array([7,2,10,2,7,4,9,4,9,8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist = np.linalg.norm(x - y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.529964086141668"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "values = np.random.randn(1000).cumsum()\n",
    "plt.plot(range(1000),values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_drawdown = np.max(np.maximum.accumulate(values) - values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.008446252634755"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_drawdown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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